ASPG

A Hybrid Intrusion Detection Approach for Cyber Attacks

Amrita Bhatnagar , Arun Giri , Aditi Sharma

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.

Vol. 13 Issue. 2 PP. 08-18, (2024)

Enhanced Intrusion Detection Using Stacked FT-Transformer Architecture

S. Phani Praveen , Thulasi Bikku , P. Muthukumar , K. Sandeep , Jampani Chandra Sekhar , V. Krishna Pratap

The function of network intrusion detection systems (NIDS) in protecting networks from cyberattacks is crucial. Many of the more conventional techniques rely on signature-based approaches, which have a hard time distinguishing between various types of assaults. Using stacked FT-Transformer architecture, this research suggests a new way to identify intrusions in networks. When it comes to dealing with complicated tabular data, FT-Transformers—a variant of the Transformer model—have shown outstanding performance. Because of the inherent tabular nature of network traffic data, FT-Transformers are an attractive option for intrusion detection jobs. In this area, our study looks at how FT-Transformers outperform more conventional machine learning (ML) methods. Our working hypothesis is that, in comparison to single-layered ML models, FT-Transformers will achieve better detection accuracy due to their intrinsic capacity to grasp long-range correlations in network traffic data. We also test the FT-Transformer model on several network traffic datasets that include various protocols and attack kinds to see how well it performs and how generalizable it is. The purpose of this research is to shed light on how well and how versatile FT-Transformers perform for detecting intrusions in networks. We aim to prove that FT-Transformers can secure networks from ever-changing cyber threats by comparing their performance to that of classic ML models and by testing their generalizability.

Vol. 13 Issue. 2 PP. 19-29, (2024)

Optimizing AI-Based Automated Security Patch Deployment in IoT Devices to Combat Zero-Day Exploits and Advanced Cyber Attacks

Abedallah Zaid Abualkishik , Nodira Zikrillaeva , Gulyamova Gulnora

This research shows a complete security design for Internet of Things (IoT) devices. It improves security by using five methods that work together. At the beginning of the process, a machine learning-based method for ranking changes is used. Then, architectures are put in place for scalable patch distribution, anomaly detection, dynamic risk assessment, and integrating threat data. Using five connected algorithms, the purpose of this research is to create a complete security framework for Internet of Things devices. Dynamic risk assessment, scalable patch delivery, integration with threat intelligence, and anomaly detection for zero-day vulnerabilities are among its characteristics. It also identifies zero-day vulnerabilities. Furthermore, it prioritises repairs using machine learning data. Every solution seeks to address a specific component of IoT security, such as dynamic risk assessments, effective patch distribution, and patch prioritisation based on vulnerability data. It is critical to maintain the Internet of Things ecosystem's safety, flexibility, and efficiency. An integrated approach provides a strong defence against cyberattacks, which is crucial for ecosystem preservation.With this system, you can get better accuracy, flexibility, and resource use than with other methods. To help explain how the methods work, charts and flowcharts are used. The ablation study indicates that each method is important because it shows how they all help keep IoT devices safe. The suggested design considers how cyber risks are always changing to protect connected devices in a lot of different places from hackers.

Vol. 13 Issue. 2 PP. 30-49, (2024)

Development of Digital Twin Technology in Hydraulics Based on Simulating and Enhancing System Performance

R. Uma Maheshwari , D. Jayasutha , Indu Nair V. , R. Senthilraja , Subash Thanappan , Ramya S.

DT digital twin technology has become an essential tool in hydraulic systems. It not only offers a virtual representation of the actual plant, but also real-time monitoring and optimization of that same machinery. Digital Twin (DT) technology has become a cornerstone in the optimization of industrial processes, particularly in the domain of hydraulic systems. For example, this research aims to use digital twin technology to detect and fix leaks in hydraulic systems. By integrating advanced simulation algorithms for accurate leak detection and performance enhancement, this study presents a comprehensive framework. Combining techniques developed from both data-driven and state-of-the-art optimization methods our approach looks to change how leaks are detected in hydraulics. Our test introduces a comprehensive framework that not only accurately identifies leaks but also employs advanced simulation algorithms for subsequent performance enhancement. By bringing together data-driven insights and cutting-edge optimization methods, our work at the frontier of revolutionizing leak detection in hydraulic systems.

Vol. 13 Issue. 2 PP. 50-65, (2024)

Boosting Financial Fraud Detection Using Parameter Tuned Ensemble Machine Learning Model

Reem Atassi , Aziz Zikriyoev , Nurbek Turayev , Sagdullayeva Gulnora Botırovna

Fraud detection in the financial industry is a challenging area as financial transactions gradually shift to digital platforms. More and more businesses such as the financial industry are operationalizing their services online as the usage of the internet is growing exponentially. Accordingly, financial fraud can increase in number and forms worldwide leading to remarkable financial losses that make financial fraud a main challenge. Threats such as irregular attacks and unauthorized access must be identified through a financial fraud detection system. Over the past few years, data mining and machine learning (ML) approaches have been widely used to address these issues. However, this technique has yet to be enhanced in terms of speed computation, identifying unknown attack patterns, and dealing with big data. This study presents Financial Fraud Detection using the Parameter Tuned Ensemble Machine Learning (FFD-PTEML) method. The FFD-PTEML incorporates multiple advanced components, such as z-score normalization for feature scaling and ensemble classification employing Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) networks. The use of z-score normalization ensures uniformity in feature distribution, improving the effectiveness and interpretability of the fraud detection technique. Furthermore, the ensemble classification model combines the strength of different neural network architectures to enhance the detection performance and resilience to complicated fraud patterns. FFD-PTEML demonstrates better performance than the classical technique through extensive experimentation on real-time financial datasets, exhibiting high sensitivity and specificity in fraudulent activity detection.

Vol. 13 Issue. 2 PP. 66-74, (2024)

Discovering Unknown Non-Consecutive Double Byte Biases in RC4 Stream Cipher Algorithm

Sura Mahroos , Rihab Hazim , AbdulRahman Kareem Oliwe , Nadia Mohammed , Yaqeen Saad , Ali Makki , Ibrahiem El Emary

RC4 is one of the most widely used stream cipher algorithms. It is fast, easy and suitable for hardware and software. It is used in various applications, but it has a weakness in the distribution of generated key bytes. The first few bytes of Pseudo-Random Generation Algorithm (PRGA) key stream are biased or attached to some private key bytes and thus the analysis of key stream bytes makes it potential to attack RC4, and there is connection between the key stream bytes that make it weak and breakable by single- and double-byte biases attack. This work shows the analysis of RC4 key stream based on its non-consecutive double byte biases by using newly designed algorithm that calculates the bias in a standard time (seconds). The results are shown that the bias of RC4 keystream is proved and got the same results that were shown in the literature with less time and discover a set of new non-consecutive double byte biases in the positions (i) and (i+n). The analysis of 256 positions is required additional requirements such as supercomputer and the message passing interface environment that are not available in Iraq, therefore; the analysis is done for 32 positions.

Vol. 13 Issue. 2 PP. 75-83, (2024)

Advancing IoT Device Security in Smart Cities: Through Innovative Key Generation and Distribution With D_F, Gf, and Multi-Order Recursive Sequences

Sanaa Ahmed Kadhim , Ruwaida Mohammed Yas , Saad A. A. Abdual Rahman

In today's mass communication landscape, security is a paramount concern, notably with the rapid expansion of the Internet of Things (IoT). Various methods aim to bolster IoT communication security, particularly by regulating access between IoT devices and networks. Encrypting data with a shared secret key is crucial, considering the limited capabilities of these devices, demanding a lightweight yet robust control mechanism. While traditional methods like Diffie-Hellman facilitated secure communication, vulnerabilities arose from modular and exponential equations. Our paper proposed a mathematical refinement of the Diffie Hellman (D_H) protocol. By leveraging GF finite fields and multi-order recursive sequences, this enhanced method aims to fortify confidentiality and complexity in exchanged keys, enabling secure data transmission while remaining efficient for resource restricted IoT devices. Validation using the Affine encryption method demonstrates considerable improvements in complexity, security, and speed. Incorporating Galois field (GF) and third-order sequencing enhances secrecy and complexity, ensuring swift computational processes.

Vol. 13 Issue. 2 PP. 84-95, (2024)

Link-Based Xcorr Normalization and Attention Mechanism for Predicting the Threats over the Network Model

V. Jemmy Joyce , K. Rebecca Jebaseeli Edna , P. Sherubha , Arivazhagi

Sensor Networks (SNs) play an essential role in upcoming technologies like the Internet of Things (IoT), where technical services are highly prone to crucial vulnerability due to attacks. This research motivates to provide a mechanism to identify the link reliability of connected sensor nodes. The privacy-preserving keys are distributed among the corresponding network nodes. When the nodes suffer from an attack, it damages the linking nodes' community. It has the nature of healing itself when the attacks are identified over the network. The self-healing nature is not so complex, and it is termed a lightweight process. A novel link-based intrusion prediction mechanism uses attention-based Deep Neural Networks (-DNN) for lightweight linkage identification and labelling. This model helps predict basic network patterns using topological analysis with better generalization. The simulation is done with Python where the proposed -DNN model outperforms the five different conventional approaches with the adoption of a benchmark dataset (network traffic) for extensive analysis. The AUC is improved in an average manner with the adoption of -DNN. This model enhances the linkage connectivity to make different connectivity processes more efficient and reach the target non-convincing. It is sensed that the proposed -DNN outperforms the existing approaches by improving the network resilience by maintaining higher energy efficiency.

Vol. 13 Issue. 2 PP. 96-108, (2024)

Hybridization of Deep Sequential Network for Emotion Recognition Using Unconstraint Video Analysis

P. Naga Bhushanam , Selva Kumar S.

The reliable way to discern human emotions in various circumstances has been proven to be through facial expressions. Facial expression recognition (FER) has emerged as a research topic to identify various essential emotions in the present exponential rise in research for emotion detection. Happiness is one of these basic emotions everyone may experience, and facial expressions are better at detecting it than other emotion-measuring methods. Most techniques have been designed to recognize various emotions to achieve the highest level of general precision. Maximizing the recognition accuracy for a particular emotion is challenging for researchers. Some techniques exist to identify a single happy mood recorded in unrestricted video. Still, they are all limited by the processing of extreme head posture fluctuations that they need to consider, and their accuracy still needs to be improved. This research proposes a novel hybrid facial emotion recognition using unconstraint video to improve accuracy. Here, a Deep Belief Network (DBN) with long short-term memory (LSTM) is employed to extract dynamic data from the video frames. The experiments conducted uses decision-level and feature-level fusion techniques are applied unconstrained video dataset. The outcomes show that the proposed hybrid approach may be more precise than some existing facial expression models.

Vol. 13 Issue. 2 PP. 109-123, (2024)

Modelling an Improved Swarm Optimizer and Boosted Quantile Estimator For Malicious Flow Monitoring And Prediction In Network

U. Harita , Moulana Mohammed

For a long time, malware has posed a significant risk to computer system security. The effectiveness of conventional detection techniques based on static and dynamic analysis is restricted due to the quick advancement of anti-detection technologies. In recent years, AI-based malware detection has increasingly been employed to combat malware due to its improved predictive ability. Unfortunately, because malware may be so diverse, it can be challenging to extract features from it, which makes using AI for malware detection ineffective. A malware classifier based on an Improved Salp Swarm optimization for feature selection and a Boosted tree with Conditional Quantile Estimation (ISSO-BCQE) is developed to adapt different malware properties to solve the problem. Specifically, the malware code is extracted, and the feature sequence is generated into a boosting tree where the feature map of the node is extracted using BCQE, where a boosting network is used to design a classifier and the method's performance is finally analyzed and compared. The results show that our model works better than other approaches regarding FPR and accuracy. It also shows that the method beats current methods with the highest accuracy of 99.6% in most detecting circumstances. It is also stable in handling malware growth and evolution.

Vol. 13 Issue. 2 PP. 124-139, (2024)

Heterogeneous Wireless Sensor Network Design with Optimal Energy Conservation and Security through Efficient Routing Algorithm

D. Bhanu , R. Santhosh

A heterogeneous wireless sensor network (H-WSN) comprises multiple sensor nodes having varied abilities, like diverse processing power and sensing range. H-WSN deployment and topology control seem to be more difficult than homogeneous WSNs. Research on H-WSNs has increased in the last few years to improve real-time sensor networks' reliability and deliver better networking services than a homogenous WSN does. When it comes to H-WSN's energy consumption and security, the major problem remains the efficient routing process.  To that end, this research aims at demonstrating how an efficient routing algorithm of hierarchical H-WSN can greatly enhance the network's performance. It is important to note that the nodes' capabilities mostly determine the suitability of a given routing algorithm. Hence, the H-WSN design issues for routing in a heterogeneous environment are discussed in this paper. This research designs an Optimal Energy Conservation and Security-aware Routing Algorithm (OECS-RA) for H-WSN using clustering and a secure-hop selection scheme. In this proposed model, the optimal cluster head selection and routing have been found through various computational stages based on the energy conservation of each sensor node. It further secures the transmission by selecting the secured node with credential factor computation and comparing each hop of the optimal route. The MATLAB simulation scenario finds the significant performance of the routing mechanism with security compared to existing models. The proposed OECS-RA gives highly recognizable throughput, lifetime, energy efficiency, and reliability. With these results, this proposed algorithm is suggested for real-time implementation in the medical industry, transportation, education, business, etc.

Vol. 13 Issue. 2 PP. 140-154, (2024)

Prediction of Skin Lesions Using Integrated Multi-Layered Network Model with Baseline Learning Approaches

Arpita Roy , Shaik Razia

Skin cancer has become more common in recent decades, raising severe concerns about world health. Creating an automated system to distinguish between benign and malignant images is challenging because of the subtle variations in how skin lesions appear. This study introduces Computer-Aided Diagnosis (CAD) system that offers high classification accuracy while maintaining low computing complexity for categorizing skin lesions. The system incorporates a pre-processing stage that uses morphological filtering to remove hair and artefacts. With the least minimum of human interaction, deep learning techniques are employed to separate skin lesions automatically. Image processing methods are currently being utilized to investigate the automated implementation of the prediction criteria for distinguishing between benign and malignant melanoma lesions. Various pre-trained convolutional neural networks (CNNs) with multi-layered (ML-CNN) are under examination for the classification of skin lesions as either benign or malignant. The best performance is achieved when RF, k-NN and XGBoost are combined, according to average 5-fold cross-validation findings. The outcomes also demonstrate that data augmentation works better than acquiring novel images for training and testing purposes. The experiment results show that the suggested diagnostic framework performs better than existing methods when used on actual clinical skin lesions, with accuracy at 97.5%, F1-score at 91.3%, precision at 96.5%, sensitivity at 89.2% and specificity at 96.7%. It also takes 2.6 seconds to complete with the MNIST dataset and accuracy at 98.2%, F1-score at 92.5%, precision at 98.4%, sensitivity at 92.3% and specificity of 97.2% with the ISIC dataset. This indicates that medical professionals can benefit from using the suggested framework to classify various skin lesions.

Vol. 13 Issue. 2 PP. 155-170, (2024)

Insider Threat Detection: Exploring User Event Behavior Analytics and Machine Learning in Security Reviews

Ruba Altuwaijiri , Hanan AlShaher

With the exponential increase in technology use, insider threats are also growing in scale and importance, becoming one of the biggest challenges for government and corporate information security. Recent research shows that insider threats are more costly than external threats, making it critical for organizations to protect their information security. Effective insider threat detection requires the use of the latest models and technologies. Although a large number of insider threats have been discovered, the field is still limited by many issues, such as data imbalance, false positives, and a lack of accurate data, which require further research. This survey investigates the existing approaches and technologies for insider threat detection. It finds and summarizes relevant studies from different databases, followed by a detailed comparison. It also examines the types of data used and the machine learning models employed to detect these threats. It discusses the challenges researchers face in detecting insider threats and future trends in the field.

Vol. 13 Issue. 2 PP. 171-181, (2024)

Computational genetic epidemiology: Leveraging HPC for large-scale AI models based on Cyber Security

Vadali Pitchi Raju , Tushar Kumar Pandey , Rajeev Shrivastava , Rajesh Tiwari , S. Anjali Devi , Neerugatti Varipallay vishwanath

To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces "GenomeMinds," a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness.

Vol. 13 Issue. 2 PP. 182-190, (2024)

Innovations in Cyber Security Algorithms for Databases Enhancing Data Retrieval and Management

Shyam S. Gupta , Pankaj Kumar , Rajeev Shrivastava , Satyabrata Jena , Tushar Kumar Pandey , Ankita Nigam

The term "Innovations in Cyber Security Algorithms for Databases Enhancing Data Retrieval and Management" refers to a book that studies novel techniques for tackling problems related to digital data. The integration of three complicated methods—DQO, DSS, and RAI—is the major focus of attention in this piece of writing. DQO makes use of machine learning to optimize query processing on the fly to meet fluctuating workloads. This is done to accommodate such workloads. To address issues pertaining to the scale of distributed systems, distributed storage systems (DSS) convey data in an effective manner by utilizing consistent hashing. The RAI algorithm adjusts the index architecture in response to the query patterns to achieve real-time flexibility. In this way, the process of looking for information that is frequently asked about is sped up. The methodology that has been suggested is superior to six different ways that are often used in terms of its adaptability, scalability, and real-time capabilities. This article will give a thorough model for improving data management in computer systems. The objective of this essay is to present the model.

Vol. 13 Issue. 2 PP. 191-198, (2024)

Cybersecurity Approaches for Securing Digital Marketing Data

Mohammad Arif , Anjali Goswami , CH. M. H. Saibaba , K. Sharada , Tushar Kumar Pandey , Ankita Nigam

The Energy Internet was enabled by quick energy sector developments due to greater digital technologies and increased environmental concerns. Energy demand management is crucial in this changing environment, as rigid models give way to more flexible ones. This research examines "Demand Dynamics in the Energy Internet" and suggests consumer and prosumer response plans. This concept regarding energy consumption and management is novel. Our work revolves around several essential aims. First, it examines the Energy Internet's role in the energy transition. It emphasizes energy savings, carbon reduction, and energy system reliability. We emphasize the need to transition away from centralized energy generation to one that is more flexible and involves active consumers and prosumers. This research examines how digital technology, particularly the Internet of Things, enables adaptable demand-side tactics. Real-time data analytics and smart meters help consumers and prosumers utilize energy efficiently. A transition like this is difficult. Data protection, hacking, and behaviour must be addressed. Our study demonstrates that these issues can be addressed immediately. Since one-size-fits-all is not adequate in this changing environment, we emphasize the need for customization to satisfy the individual demands of multiple parties, including conventional customers and prosumers. It also discusses energy Internet-targeted response strategies and their possibilities. We can reduce energy usage and make energy more sustainable, efficient, and consumer-focused by switching from passive consumption to active involvement and control.

Vol. 13 Issue. 2 PP. 199-206, (2024)

Enhancing Agricultural Productivity in Uzbekistan: Arduino-Based IoT Framework for Sunflower Seed Yield Improvement through Air and Soil Moisture Monitoring

Danish Ather , Abu B. Bin Abdul Hamid , Noor I. Binti Ya’akub , Rajneesh Kler

The core theme of the current investigation is to explore the application of an IoT framework protocol based on an Arduino platform designed to optimize sunflower seed production in Uzbekistan based on the levels of air quality and soil moisture. In essence, the need is to give best actionable intelligence to farmers and the stakeholders in the agricultural sector on crop growing opportunities. The above proposed system involves the use of air quality sensors MQ-135 for instance, and soil moisture sensors. The sensors are connected to Arduino boards to collect necessary data and measurements are recorded every 30 minutes using available WiFi and Bluetooth modules for continuous monitoring. The simulation reveals air quality data of the sunflower fields of the present scenario to be an average at PM2.5 is of 75 µg/m³, which poses danger to the wellbeing of plants. It is further expected that the use of MQ-135 air quality sensors will decrease the overall average of PM2.5 to 45 µg/m³, the local authorities managed to cut emissions by 40% as part of the EU plan. At the present time, the content of the field moistures is 15 % VWC, which is not favorable for sunflower development. Soil moisture sensors for accurate irrigation control is another advance that requires soil moisture levels to rise to 25% vadium weight (VWC), up from 66. 7% improvement. Therefore, it means that the yields from the sunflower seeds are expected to rise from the current average of 1, 500Kg/ha to 1, 875 Kg/ha, which is a 25% enhancement. These results imply that IoT systems developed on the Arduino platform may be used to oversee environmental alteration and increase the agricultural crop yield by a wide margin. The possibility was identified to achieve significant outcomes in increasing sunflower seed production based on this framework when implemented on a larger scale for the benefit of Uzbek farmers.  

Vol. 13 Issue. 2 PP. 207-219, (2024)

Journal of Cybersecurity and Information Management

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